Global inventory networks become more and more congested due to the increase of global demands for goods. The workload in logistical networks is extremely high during the peak hours. A congested situation could cause many negative effects such as delays, shortages or even cancellations. Thus, evaluation of inventory needs from a global perspective before the scheduled transports is important to stock operators, logistical carriers, shops and the consumer.
Evaluating inventory needs is a difficult task as there are many random factors. All of those factors could have significant influences on the inventory needs. Currently available software provides inventory optimization from a local perspective, that is optimizing single warehouses costs and demands. Thus, a need exists for a model and system that is able to predict inventory needs in a global distributed logistical network.